Takes a set of point, linear, polygonal, and/or aggregate features, and calculates the Topfer Index based on the bounding box of the input features.
The input features may be partitioned into groups based on attribute values using the Group By parameter, and one bounding box feature is output for each group. If the Group By parameter is not specified, then all input features will be processed together and a single bounding box will be output. If a given bounding box has zero area, it will become a line or a point.
To avoid the appearance of overcrowding or sparsity, maps drawn at different scales sometimes require a different level of detail. The Topfer Index is a measure used to predict how many features should be used at a new scale. The formula is given by:
n_dest = n_src * sqrt(M_src / M_dest)
where:
n_dest is the number of features that should be shown at the destination scale, also referred to here as the “Topfer Index.” This is the value that this transformer will compute.
n_src is the number of features shown at the source scale (in this case, the number of features received per group).
M_dest is the denominator of the destination scale (taken from a parameter).
M_src is the denominator of the source scale (taken from a parameter).
Parameters
Transformer
Group By
One Topfer Index will be computed for each group. It will be output on each feature.
Group By Mode
Process At End (Blocking): This is the default behavior. Processing will only occur in this transformer once all input is present.
Process When Group Changes (Advanced): This transformer will process input groups in order. Changes of the value of the Group By parameter on the input stream will trigger processing on the currently accumulating group. This may improve overall speed (particularly with multiple, equally-sized groups), but could cause undesired behavior if input groups are not truly ordered.
Considerations for Using Group By
There are two typical reasons for using Process When Group Changes (Advanced) . The first is incoming data that is intended to be processed in groups (and is already so ordered). In this case, the structure dictates Group By usage - not performance considerations.
The second possible reason is potential performance gains.
Performance gains are most likely when the data is already sorted (or read using a SQL ORDER BY statement) since less work is required of FME. If the data needs ordering, it can be sorted in the workspace (though the added processing overhead may negate any gains).
Sorting becomes more difficult according to the number of data streams. Multiple streams of data could be almost impossible to sort into the correct order, since all features matching a Group By value need to arrive before any features (of any feature type or dataset) belonging to the next group. In this case, using Group By with Process At End (Blocking) may be the equivalent and simpler approach.
Note: Multiple feature types and features from multiple datasets will not generally naturally occur in the correct order.
As with many scenarios, testing different approaches in your workspace with your data is the only definitive way to identify performance gains.
Parameters
Topfer Index Attribute
The name of the attribute where the Topfer Index should be stored.
Source Scale
The denominator for the scale of the source dataset. For example, if the scale is 1:250000, the Source Scale should be 250000. See the Topfer Index equation in the description.
Destination Scale
The denominator for the scale of the destination dataset. For example, if the new potential scale is 1:500000, the Destination Scale should be 500000. See the Topfer Index equation in the description.
Editing Transformer Parameters
Using a set of menu options, transformer parameters can be assigned by referencing other elements in the workspace. More advanced functions, such as an advanced editor and an arithmetic editor, are also available in some transformers. To access a menu of these options, click 
beside the applicable parameter. For more information, see Transformer Parameter Menu Options.
Transformer Categories
To retrieve the bounds of a feature into attributes, use the BoundsExtractor.
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